1. Technical Field
The disclosure relates generally to signal processing and in particular to improved signal averaging. Still more particularly, the disclosure relates to a computer implemented method, apparatus, and computer usable program code for applying rank ordering and closeness thresholds to select signal samples or ignore noise samples in signal averaging to enhance a signal-to-noise ratio of a target signal.
2. Background Description
A detector may be a device for detecting, receiving, capturing, recording, or registering a signal containing target data. For example, a detector may include an infrared (IR) detector for detecting infrared radiation signals. An infrared detector can be used in night vision equipment, amongst many other uses, to detect warm targets. However, the data signal captured by a detector may contain noise or clutter in addition to the target or desired signal data.
Noise may be the addition of unwanted data to the signal stream being received by a detector. Noise may be deliberate, as in jamming a radio signal. In other cases, noise may result from equipment problems, such as equipment instability, incorrect antenna modulation, or pointing errors. Noise can also occur due to natural interference, such as signal clutter and radiation induced impulse noise or “spikes”.
Signal clutter may be a source of unwanted signals, such as the ground, the sea, weather, buildings, birds, insects, or any other surface, volume or point clutter resulting in unwanted signal data. For example, if radar is utilized to track an airplane, radar signal data regarding the airplane may be target signal data. A radar signal data generated as a result of birds flying within range of the radar may be unwanted signal clutter.
A signal processor may be a hardware device that can utilize various methods to separate target signal data from unwanted noise on the basis of signal characteristics, such as signal amplitude. An important component in a variety of signal processing systems is a signal averager. In signal averaging, a recurring waveform signal can be divided into segments of an appropriate length, for example, one second segments. The segments can be averaged to reduce the amplitude of noise that is uncorrelated with the target signal. The assumption is that target signal data growth with averaging exceeds noise growth and the signal-to-noise (SNR) ratio increases or improves.
However, if the noise is non-stationary noise, conventional signal averaging can degrade the signal-to-noise ratio. In other words, if the noise spikes are not distributed at fixed time or position in the original signal, the noise in the averaged signal can be greater than in the original signal segments. In such a case, the target signal can be more obscured in the averaged signal.
Filtering can be used to isolate target data from noise. However, current linear filtering (matched filtering) procedures require a prior knowledge of the power spectrum of signal, noise, and clutter. Linear filtering generally performs poorly in intermittent, high amplitude spiky (non-stationary) noise because noise and clutter spikes may not be ignored. Instead, noise and clutter spikes may be suppressed according to the convolution characteristics of the filtering waveform. Convolution refers to the transforming of an input data sample to an output sample or group of samples according to the characteristics of the linear filtering kernel. These types of signal matched linear filters do not directly support ignoring data from non-stationary, intermittent noise spikes because the spectrum of intermittent, spiky noise may be a broad band “white” noise spectrum.
Non-linear blanking procedures can be used to filter noise. However, these procedures may require identification and substitution of undesired noise spikes based on the detectable exceedance of the spikes above a slowly varying signal baseline. This situation is may be a “Catch 22” situation because signal baselines that are estimated in intermittent, spiky (non-stationary) noise environments can be highly variable because noisy spikes are not suppressed.
Powerful change detection signal processing procedures that save target signals that move or change their target features can be used to cancel correlated noise or clutter backgrounds on frame to frame subtraction. However, these techniques may not work for stationary target signals imbedded in uncorrelated, non-stationary, “white” noise and clutter.
Therefore, it would be advantageous to have an improved method, apparatus, and computer usable program code for averaging of signal samples that ignores non-stationary noise and clutter interference signal samples in the averaging process.